no code implementations • 19 Jul 2023 • Peizhen Yang, Xinke Shen, Zongsheng Li, Zixiang Luo, Kexin Lou, Quanying Liu
Specifically, we trained neural networks (i. e., CNN, vanilla RNN, GRU, LSTM, and Transformer) to predict future EEG signals according to historical data and perturbed the networks' input to obtain effective connectivity (EC) between the perturbed EEG channel and the rest of the channels.
no code implementations • 10 Feb 2023 • Mengmeng Wang, Zhiqiang Han, Peizhen Yang, Bai Zhu, Ming Hao, Jianwei Fan, Yuanxin Ye
In this letter, a novel method for change detection is proposed using neighborhood structure correlation.
1 code implementation • 25 Nov 2022 • Zheng Wang, Xiaoliang Fan, Jianzhong Qi, Haibing Jin, Peizhen Yang, Siqi Shen, Cheng Wang
Second, constrained by the far-distance in data distribution of the sampled clients, we further minimize the variance of the numbers of times that the clients are sampled, to mitigate long-term bias.
no code implementations • 27 Sep 2021 • Xu Yan, Xiaoliang Fan, Peizhen Yang, Zonghan Wu, Shirui Pan, Longbiao Chen, Yu Zang, Cheng Wang
Representation learning on temporal interaction graphs (TIG) is to model complex networks with the dynamic evolution of interactions arising in a broad spectrum of problems.